Author

Bo-Wen Liu

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#machine learning Preprint Sep 2026

Noise2Noise Revisited: Training Pair Distributions Dominate Loss Choice in Self-Supervised Denoising

Noise2Noise (N2N) trains denoisers on pairs of independently corrupted observations, eliminating clean references. We stress-test two natural conjectures about why the L1 loss outperforms L2 here. First, the hypothesis that the L1 loss confers robustness via parameter sparsity confuses the loss with Lasso regularizatio...

Ding-Yan Shang, Zhen-Yu Xu, You Wang et al. · 0 citations

Jul 2026

Context-Masked Truncated Reasoning Audits for Answer-Key Dependence in LLM Tutors

Context masking is established as necessary for attributing early answer availability to an explanation rather than its hidden input when early-prefix evidence disappears after masking.

Bo-Nan Shen, Ding-Yan Shang, You Wang et al. · arXiv.org · 0 citations

Jul 2026

Safety, or Just Capability? A Validity Audit of Agent-Safety Benchmarks

Naming the benchmark, metric, target behavior, and model panel is the minimum a safety claim needs, and both instruments score harmful compliance, so this is evidence of convergent validity rather than general safety.

You Wang, Xiao Han, Ding-Yan Shang et al. · arXiv.org · 1 citation

Preprint Aug 2026

$R^3$-Bench: LLMs Struggle with Resource-Rational Reasoning under Shared Budgets

$R^3$-Bench is introduced, which evaluates six-problem suites under shared budgets across mathematics, competitive programming, and abstract reasoning in tool-free and agentic settings, and exposes a persistent gap between demonstrated competence and shared-budget realization.

Peisong Wang, Zhiwei Ma, Bo-Wen Liu et al. · 0 citations